Abstract: Particle tracking (PT) is a powerful biophysical tool for elucidating molecular interactions, transport phenomena and rheological properties in complex biological environments. Unfortunately, PT remains a niche tool in life and physical sciences with a limited user base, in large part due to significant time and technical constraints in extracting accurate time-variant positional data from recorded movies. These constraints are exacerbated in experiments with low signal-to-noise ratios or substantial heterogeneity, as frequently encountered with nanoparticles and pathogens in biological fluids. Currently available software that attempts to automate the movie analysis process rely almost exclusively on assigning static image filters based on specific intensity, pixel size and signal-to-noise ratio thresholds. Unfortunately, when applied to actual experimental data with substantial spatial and temporal heterogeneity, the current software generally produces substantial numbers of false positives (i.e. tracking artifacts) or false negatives (i.e. missing actual traces), and frequently both. Frequent user intervention is thus required to ensure accurate tracking even when using sophisticated tracking software, markedly reducing experimental throughput and resulting in substantial user- to-user variations in analyzed data. The time required for accurate particle tracking analysis makes PT experiments exceedingly expensive compared to other commonly used experimental techniques in life sciences. These same tracking analysis limitations have effectively precluded investigators from undertaking more sophisticated 3D PT, even though the microscopy capability to obtain such movies is readily available and critical scientific insights can be gained from 3D PT. To circumvent the challenges with currently available particle tracking software, we have developed a new approach for particle identification and tracking, based on machine learning and convolutional neural networks (CNN). CNN is a type of feed-forward artificial neural network designed to process information in a layered network of connections that mimics the organization of real neural networks in the mammalian retina and visual cortex. Unlike most CNN imaging models that are trained to make predictions on static images, we have trained our CNN to input adjacent frames so that each prediction includes information from the past and future, thus effectively performing convolutions in both space and time to infer particle locations. Similar principles of image analysis are now being harnessed by developers of autonomous vehicle technologies to distinguish the motions of different objects on the road. We have applied our CNN tracking algorithm to a wide range of 2D movies capturing dynamic motions of nanoparticles, viruses and highly motile bacteria, achieving at least 30-fold time savings with virtually no need for human intervention while maintaining robust tracking performance (i.e. low false positive and low false negative rates). In this STTR proposal, we seek to focus on further optimization and testing of our neural network tracking platform for 2D PT, including the use of cloud computing (Aim 1), and extending our neural network tracker to enable accurate 3D PT (Aim 2). Our vision is to popularize PT as a research tool among researchers by minimizing the time and labor costs associated with PT analysis.